Data: Continuous vs. Categorical

Oct 23, · Categorical data, as the name implies, are usually grouped into a category or multiple categories. Similarly, numerical data, as the name implies, deals with number variables. Categorical Data Definition. Categorical data is a collection of information that is divided into groups. Sep 28, · Let’s sum the key characteristics of categorical data we learned above: Categorical data is divided into groups or categories. The categories are based on qualitative characteristics. There is no order to categorical values and variables. Categorical data can .

Sign in. We covered various feature engineering strategies for dealing with structured continuous numeric dat a dafa the what does the mask of truth do article in this series.

In this article, we will look at another type of structured data, which is discrete in nature and is popularly termed as categorical data. Dealing with numeric data is often easier than categorical catgorical given that we do not have to deal with additional complexities of the semantics pertaining to each category value in any data attribute which is of a categorical type.

Do check it out for a quick refresher if necessary. In short, machine learning algorithms cannot work directly with categorical data and you do need to caegorical some amount of engineering and transformations on this data before caregorical can start modeling on your data. Typically, any data attribute which is categorical in nature represents discrete values which belong to a specific finite set of categories or classes.

These are also often known as classes or labels in the context of attributes or variables which are to be predicted by a model popularly known as response variables. These discrete values can be text or numeric in nature or categoricql unstructured data like images!

There are two major classes of categorical data, nominal and ordinal. In any nominal categorical data attribute, daat is no concept of ordering amongst the values of that attribute.

Wat a simple example of weather categories, as depicted in the following figure. Similarly movie, music and video game genres, country categorial, food and cuisine types are other examples of nominal categorical attributes. Ordinal categorical attributes have some sense or notion of order dataa its values.

For instance look at the following figure for shirt sizes. Shoe sizes, education level and employment roles are some other examples of wat categorical attributes. While a lot of advancements have been made in categoriical machine learning frameworks to accept complex categorical data types like text labels. Typically any standard workflow in feature engineering involves some form of transformation of these categorical values into numeric labels and then applying some encoding scheme on these values.

We categorlcal up the necessary essentials before getting started. Nominal attributes consist of discrete categorical values with no notion or sense of order amongst them. The idea here is to transform these attributes into a more representative aa format which iz be easily understood by downstream code and pipelines. This dataset is whhat available on Kaggle as well as in my GitHub repository. It is quite evident that this is a nominal categorical attribute just like Publisher and Platform. We can easily get the list of unique video game genres as follows.

This tells us that we have 12 distinct video game genres. We can now generate a label encoding scheme for mapping each category to a numeric value by leveraging scikit-learn.

Thus a mapping scheme has been generated where each genre value is mapped to a number with the help of the LabelEncoder object gle.

These labels can be used directly often especially with frameworks like scikit-learn wha you plan to use them as response variables for prediction, however as discussed earlier, we will need an additional step of encoding on these before we can use them as features. Ordinal attributes are categorical attributes with a sense of order amongst the values. Hence they have a sense of order amongst them.

In general, there is no generic module or function to map and transform these features into numeric representations based on order automatically. It is quite evident from the above code that the map … function from pandas is quite helpful in transforming this ordinal feature.

You might be wondering, we just converted categories to numerical labels in the previous section, why on earth do we need this now? The reason is quite simple. Considering video game genres, if we directly fed the GenreLabel attribute as a feature in a machine learning model, it would consider it to be a continuous numeric feature thinking value 10 Sports is greater than 6 Racing but that is meaningless because the Sports genre is certainly not bigger or smaller than Racingthese are essentially different values or categories which cannot be compared directly.

Hence we need an additional layer of encoding schemes where dummy features are created for each unique value or category out of all the distinct categories per attribute. Considering we have the numeric representation of any categorical attribute with m labels after transformationthe one-hot encoding scheme, encodes or transforms the attribute into m binary features which can only contain a value of 1 or 0.

Each observation in *what is a categorical data* categorical feature is thus converted into a vector of size m with only one **what is a categorical data** the values as 1 indicating it as active. The first step is to transform these attributes into numeric representations based on what we learnt earlier.

But we encode each feature separately, to make things easier to understand. Besides this, we can also create separate data frames and label them accordingly.

Thus you can see that 6 dummy variables or binary features have been daata for Generation and 2 for Legendary since those are the total number of distinct categories in each of these attributes respectively. Active state of a category is indicated by the 1 value w one of these dummy variables which is quite evident from the above data frame. Consider you built this encoding scheme on your training data and built some model and now you have some new data *what is a categorical data* has to be engineered for features before predictions as follows.

Remember our workflow, first we do the transformation. The above data frame depicts the one-hot encoding scheme applied on the Generation attribute and the results are same as compared to the earlier results as expected. The dummy coding scheme is similar to the one-hot encoding scheme, except in the case of dummy coding scheme, when applied on a categorical feature with m distinct labels, we get m - 1 binary features. Thus each value of the categorical variable gets converted into a vector of size m - 1.

If you want, you can also choose to drop the last level binary encoded feature Gen 6 as follows. Based on the whwt depictions, it is quite clear that how to deep water tomato plants belonging to the dropped feature are represented how to use teamviewer vpn connection a what were you expecting an exploding pen of zeros 0 like we discussed earlier.

The effect coding scheme is actually very similar to the dummy coding scheme, except during the encoding process, the encoded features or feature vector, for the category values which represent dsta 0 in the dummy coding scheme, is replaced dafa -1 in the effect coding scheme.

This will become clearer with the following example. The encoding schemes we discussed so far, work quite well on categorical data in general, but they start causing problems when the number of distinct categories in any feature becomes very large.

Essential for any categorical feature whaf m distinct labels, you get vategorical separate features. This can easily increase the size of the feature set causing problems like storage issues, model training problems wwhat regard to time, space and memory.

Hence we need to look towards other categorical data feature engineering schemes for features having a large number of possible categories like IP addresses.

The bin-counting scheme is a useful scheme for dealing with categorical variables having many categories. In categoircal scheme, instead of using the actual label values for encoding, we use probability based statistical information about the value and the actual target or response value which we aim to predict in our modeling efforts.

Using this information, we can encode an input feature which depicts that if the same IP *what is a categorical data* xata in the future, what is the probability value of a DDOS attack being caused.

This scheme caregorical historical wat as a pre-requisite and is an elaborate one. Depicting this with a complete example would be currently difficult here but there are several resources how to pay visa bill online which you can refer to for the same.

The feature hashing scheme is another useful feature engineering scheme for dealing with large scale categorical features. In this scheme, a hash function is how to watch two things at once on tv used with the number of encoded features pre-set as a vector of pre-defined length such that the hashed values of the features are used as indices in this pre-defined vector and values are updated accordingly.

Since a hash function maps a large number of values into a small finite set of values, multiple different values might create the same hash which is termed as collisions. Typically, a signed hash function is used so that the sign of the value obtained from the hash is used as the sign of the value which is stored in the final feature vector at the appropriate index.

This should ensure lesser collisions and lesser accumulation of error due to collisions. Hashing schemes work on strings, numbers and other structures like vectors.

We can pre-define the value of b which becomes the final size of the encoded feature vector for each categorical attribute that we encode using the feature hashing scheme.

We can see that there are a total of 12 genres of video games. If we used a one-hot encoding scheme on the What are capital markets in real estate feature, we would end categoriacl having 12 binary features.

We will pre-define the final feature vector size to be 6 in this case. Based on the above output, the Genre categorical attribute has been encoded whar the hashing scheme into 6 features instead of We can also see that rows 1 and 6 fata the same genre of games, Platform which have been rightly encoded into the same feature vector. These examples should give you a categoricap idea about popular strategies for feature engineering on discrete, categorical data. If you read Part 1 of this series, you would have seen that it is slightly challenging to work with categorical data as compared to continuous, numeric data but definitely interesting!

We also talked about some ways to handle large feature spaces using feature engineering but you should also remember that there are other techniques including feature selection and dimensionality reduction methods to handle large feature spaces. We will cover some of these daat in a later article. Next up will be feature engineering strategies for unstructured text daha.

Stay tuned! To read about daata engineering strategies for continuous numeric data, check out Part 1 of this series! All the code and datasets used in this article can be accessed from my GitHub. The actegorical is also available as a Jupyter notebook. Every Thursday, the Variable delivers the very best of Towards Data Science: from hands-on tutorials and cutting-edge research to original features you don't want to miss. Take a look. Review our Privacy Policy for more information about our privacy practices.

Check your inbox Medium sent you an email at to daat your subscription. Your home for data science. A Medium publication sharing concepts, ideas and codes. Get started. Open in app. Sign in Get started. Get started Open in app. Strategies for working with discrete, categorical data. Dipanjan DJ Sarkar. Introduction We **what is a categorical data** various feature engineering strategies for dealing with structured continuous numeric dat a in the previous article in this series.

All the code and datasets used in this article can be accessed from my GitHub The code is also available as a Jupyter notebook. Thanks to Ludovic Benistant. Sign up ls The Variable. Get this newsletter.

Transforming Nominal Attributes

Sep 20, · Categorical Data, sometimes called qualitative data, are data whose values describe some characteristic or category. For example, a survey could ask a random group of people: What is your lucky day of the week? Nov 23, · Categorical data is when numbers are collected in groups or categories. Categorical data is also data that is collected in an either/or or yes/no situation. Key Terms. Apr 05, · Categorical data is everything else. As the name suggests, categorical data is information that comes in categories—which means each instance of it is distinct from the others. Names are an example of categorical data, and my name is distinct from your name.

In mathematical and statistical analysis, data is defined as a collected group of information. Information, in this case, could be anything which may be used to prove or disprove a scientific guess during an experiment. Data collected may be age, name, a person's opinion, type of pet, hair colour etc. Although there is no restriction to the form this data may take, it is classified into two main categories depending on its nature—namely; categorical and numerical data.

Categorical data, as the name implies, are usually grouped into a category or multiple categories. Similarly, numerical data, as the name implies, deals with number variables. Categorical data is a collection of information that is divided into groups. This data is called categorical because it may be grouped according to the variables present in the biodata such as sex, state of residence, etc. One can neither add them together nor subtract them from each other. There are two types of categorical data, namely; the nominal and ordinal data.

Examples of nominal data include name, hair colour, sex etc. Mostly collected using surveys or questionnaires, this data type is descriptive, as it sometimes allows respondents the freedom to type in responses. Although this characteristic helps in arriving at better conclusions, it sometimes poses problems for researchers as they have to deal with so much irrelevant data. Read Also: What is Nominal Data? Although mostly classified as categorical data, it is said to exhibit both categorical and numerical data characteristics making it in between.

Its classification under categorical data has to do with the fact that it exhibits more categorical data character. Some ordinal data examples include; Likert scale, interval scale, bug severity, customer satisfaction survey data etc. Each of these examples may have different collection and analysis techniques, but they are all ordinal data.

There consist of two categories of categorical data, namely; nominal data and ordinal data. Nominal data, also known as named data is the type of data used to name variable, while ordinal data is a type of data with a scale or order to it.

Categorical data is qualitative. That is, it describes an event using a string of words rather than numbers. Categorical data is analysed using mode and median distributions, where nominal data is analysed with mode while ordinal data uses both. In some cases, ordinal data may also be analysed using univariate statistics, bivariate statistics, regression applications, linear trends and classification methods. It can also be analysed graphically using a bar chart and pie chart.

A bar chart is mostly used to analyse frequency while a pie chart analysis percentage. This is done after grouping into a table. In the case of ordinal data, which has a given order or scale, the scale does not have a standardised interval. This is not applicable for nominal data. Although categorical data is qualitative, it may sometimes take numerical values.

However, these values do not exhibit quantitative characteristics. Arithmetic operations can not be performed on them. Categorical data may also be classified into binary and non binary depending on its nature. What is your household income? This is a closed ended nominal data example. The level of education of a respondent may be requested for when filling forms for job applications, admission, training etc. This is used to assess their qualification for a specific role.

Consider the example below:. Examples of Categorical Data. What is your highest level of education? This is also a closed-ended nominal data example. Respondents are asked for their gender when filling out a biodata. This is mostly categorised as male or female, but may also be nonbinary. For example:.

This is a binary and closed-ended nominal data example. This is a nonbinary and open-closed ended nominal data example.

After rendering service to customers, businesses like to get feedback from customers regarding their service to improve. For example;. Kindly rate your customer service experience with us. The above is an example of an ordinal data collection process. The responses have a specific order to them, listed in ascending order. When doing competitive analysis research, a soap brand may want to study the popularity of its competitors among their target audience.

In this case, we have something of this nature:. Which of the following soap brands are you familiar with? This is a multiple-choice nominal data collection example. This is a key categorical data example used in profiling a respondent. Although not accurate, a person's hair colour together with some racially prominent traits may be used to predict whether the person is black, caucasian, Hispanic, etc. This is a closed-ended example of nominal data. Online surveys are commonly used to carry out investigations on certain topics.

The data gathered in some cases are categorical. How many siblings do you have? The above is an example of an open-ended nominal data collection form. The response may be quantitative but will possess qualitative properties. This example may be used by a therapist or psychologist when examining a patient for mental illness.

It is usually collected together with some important data that may affect a person's mental health. Rate your happiness level on a scale of This is an ordinal data example. Companies who want to improve employee productivity may use this method to discover what motivates employees to work better. What motivates you to work better? Others specify. This is a closed open-ended nominal data collection example. Travel and tourism companies ask their customers or target audience this question to inform marketing strategies.

What are your motives for travelling? An event planning company may use an interval scale to get the demographics of attendees of a particular event.

It is also used by Instagram and Facebook to give audience insights. In which of the following age bracket do you fall? This is an example of ordinal data collection. Some timesheet calculator tool collects real-time employee location so that employers can know which employee is at work and which one isn't. This is also used in several other cases. When software companies perform quality assurance testing to discover bugs in the software, the bugs are treated according to their severity level.

When a bug bounty hunter submits a bug to a company, it is given a severity level like critical, medium or low. This is an example of ordinal data. How will you rate the desert served tonight? This is a 5 point Likert scale , a common example of ordinal data. Employees measure a job applicant's proficiency level in skills required to perform well in the job. This helps in choosing the best applicant for the job. What is your proficiency level in excel?

This is a simple example of ordinal data. Want to Collect Categorical Data? A categorical variable is a variable type with two or more categories. Sometimes called a discrete variable, it is mainly classified into two nominal and ordinal. For example, if a restaurant is trying to collect data of the amount of pizza ordered in a day according to type, we regard this as categorical data.

When gathering the data, the restaurant will group the number of orders according to the type of pizza e. In this case, the type of pizza ordered is the Categorical variable. Categorical Data Variables are divided into two, namely; ordinal variable and nominal variable. There are two main categories of nominal data variables, namely; matched and unmatched category.

Below are the tests carried out on each category:. There are two main categories of ordinal data variables, namely; matched and unmatched category. When applying for jobs, employers collect both nominal and ordinal data.

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